To learn the seq2seq model, it's straight-forward to use each pair of utterances in C s as training data for the model.

the goal of our paper is to generate diverse augmented data and the usefulness of less diverse pair (like give me the <distance> route to <poi type> and find me the <distance> route to <poi type> in Figure 1) is arguable.


If we don't filter the alike instances when training the seq2seq model, the drop of performance is a 0.65 F-score.

larger number of new utterances with smaller edit distances are yielded which indicates that more noise is introduced when the training data of the seq2seq model is not properly filtered.


Beyond these classic approaches, adding noise to the image, randomly interpolating a pair of images (Zhang et al., 2018) are also proposed in previous works.

these signal transformation approaches are not directly applicable to language because order of words in language may form rigorous syntactic and semantic meaning (Zhang et al., 2015).


Its ability of generating adversarial examples is attractive for data augmentation.

it hasn't been tried in data augmentation beyond computer vision (Antoniou et al., 2018).


In this paper, we hypothesize that dialogue acts improve conversation modeling.

it is not always possible that such dialogue acts are available in practice, and it would be ideal to predict dialogue acts first (Kumar et al., 2017), and then use them for next utterance generation/retrieval; having a model where both tasks, i.e.


Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization.

many DRL-based policies are not sample-efficient.


DRL-based models are often more expressive and computational effective.

these deep models are not sample-efficient and not robust to errors from input modules of SDS.


REINFORCE (Williams et al., 2017), advantage actor-critic (A2C) (Fatemi et al., 2016).

compared with GPRL, most of these models are not sample-efficient.


Neural networks for the approximation of value functions have long been investigated (Lin, 1993).

these methods were previously quite unstable (Mnih et al., 2013).


Note that, in input module and communication module, the same types of nodes share parameters, which may speed up the learning process.

in output module, in oder to capture the specific characteristics of each node, they don't share parameters.


usually the graph is not known in practice, and the hypothetical structure is not guaranteed to be optimal.


The reason is that each representation of the individual modality encodes specific knowledge and is complementary, an aspect that can be explored to facilitate understanding on the entire meaning of the content.

this task could be extremely challenging because we need to explore single-modal information deliberately and jointly learn the intrinsic correlation among various modalities.


(16) 6: until convergence Our proposed framework MEMD is seemingly similar to the many-to-many setting in multi-task sequence-sequence learning (Luong et al., 2015).

there are obvious distinctions between MEMD and multi-task sequence-sequence learning.


In addition, another phenomenon observed is that the longer the source sentence is, it is easier to ignore important information for RNNsearch .

as can be seen from the boldfaced sections marked in results generated with RNMT, proposed model with CNN could captures more source information successfully.


NMT yields the state-of-the-art translation performance in resource rich scenarios (Bojar et al., 2017;Nakazawa et al., 2017).

currently, high quality parallel corpora of sufficient size are only available for a few language pairs such as languages paired with English and several European language pairs.


For instancelevel interpolation, the most related method is to assign a weight in NMT objective function (Chen et al., 2017a;Wang et al., 2017b).

the model structures of SMT and NMT are quite different.


Domhan and Hieber (2017) propose a method similar to the deep fusion method (Gülçehre et al., 2015).

unlike training the RNNLM and NMT model separately (Gülçehre et al., 2015), Domhan and Hieber (2017) train RNNLM and NMT models jointly.


It has been shown that CNN based NMT and the Transformer significantly outperform the state-of-the-art RNN based NMT model of  in both the translation quality and speed perspectives.

currently, most of the domain adaptation studies for NMT are based on the RNN based model (Bahdanau et al., 2015).


With increasing access to digital historical text, the processing of these historical texts is attracting more and more interest.

in contrast to modern text, historical text processing faces more challenges.


There are 84.4% and 75.8% historical spellings that are identical to their modern spellings in German and English, respectively.

the unchanged rate is only 17.1% in Hungarian.


That is to say, the average edit distance of incorrectly normalized spellings will be larger compared to the average edit distance before normalization.

hungarian is the exception in Table 8, which indicates that spellings with longer edit distance are more likely to be normalized close to modern spellings in hungarian.


Since SMT models are more focused on a local context, the SMT models choose 'tok' rather than 'tuk'.

in terms of accuracy, it is still hard for NMT models to exceed SMT models in Swedish.


This can be attributed to the small size of CORPUS-26 TRAIN data used to train the 5-gram LM and the large number of classes.

adding 5-gram character LM scores as features (row n.) beats the baseline scores (row a.).


This is therefore a coarse abstraction of the structured prediction tasks presented in this paper.

this constitutes the most straight-forward task in emotion analysis.


The same applies to experiencer: if the head of the governing phrase is an emotion, then the head of the current phrase is a potential experiencer.

due to variability of emotion expressions, this cannot always be the case.


At the same time, the resource we present provides interesting and valuable insights in the language of emotion expression and, therefore, is useful to the community of linguists who are interested in the study of linguistic properties of emotions.

we also note that developing such a resource has its limitations: Due to the subjective nature of emotions, it is challenging, if not impossible, to come up with an annotation methodology that would lead to less disparate annotations, especially if in addition to emotion, other categories should be annotated together with roles.


Fictional texts are highly metaphoric and full of allusions and metonymies, which requires thoughtful reading (often reading between the lines) and a broader context.

this is something that our annotators do not have: all the context they have at their disposal is a triple of sentences, each of which can rely on information that is available in other parts of the book, but not in the annotation unit.


Clearly, the problem of string transduction subsumes the problem of sequence labeling, as one can always try to learn a mapping f from a given training data of strings of identical length.

there is a significant distinction that is usually made between the two, as string transduction can re-write a string into a completely different string, while sequence labeling has a stronger notion of locality.


We found out that while it perhaps prevents insertions in legitimate positions, if the frequency threshold is low enough, it does not have an adverse effect.

it prevents adding spurious insertions that cannot be correctly recovered as deletions.


The intermediate string representation is intended to correct mistakes while actually relying on the type of substitutions, deletions and insertions done by users of social media.

it is still prone to character-based mistakes, since it is a learned component.


A simple way of retrieving parallel sentences from comparable articles is to align the sentences in source and target pages together using a sentence alignment algorithm (Gale and Church, 1993;Fung and Church, 1994;Wu, 1994;Moore, 2002).

these aligners are designed to align parallel corpora in which the source and target sentences are in the same order (i.e., no cross-alignment) or in proximity to each other and in which each sentence has only one matching sentence (i.e., no many-tomany alignment).


As we discussed earlier, we are interested in perfect parallel sentences which are clustered in the highest σ regions.

partial parallel sentences in lower regions can be used for certain purposes too.


Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models.

this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing.


Unfortunately, Google Ngrams is inherently limited in that it does not contain full texts.

for many cases, this corpus was enough, and its usage as the source of diachronic data continued in Mitra et al.


Ideally, diachronic approaches should be evaluated on human-annotated lists of semantically shifted words (ranked by the degree of the shift).

such gold standard data is difficult to obtain, even for English, let alone for other languages.


(3) (Li et al., 2016a;Chen and Ren, 2017) take into account the content and static characteristics of network structures to deduce topics.


(2017) model the entailment task as the seq2seq generation problem and enforce sharing of the same decoder between summarization and entailment.

the entailment task is more reasonable to be considered as a multi-label classification problem rather than a generation problem.


Our system is based on bidirectional recurrent neural networks that can learn sentence representations in a shared vector space by explicitly maximizing the similarity between parallel sentences.

to previous approaches, by leveraging these continuous vector representation of sentences we remove the need to rely on multiple models and specific feature engineering.


Hence, our training set contains is a target sentence of M tokens, and y i is the label representing the translation relationship between s S i and s T i , so that The advantage of negative sampling is its simplicity.

relying only on randomness to select negative sentence pairs makes most of the examples very non-parallel and easy to classify.


The most reliable way to create test sets to compare different approaches would be to have professional translators manually annotate parallel sentences from comparable corpora.

this option is expensive and impractical.


We see that having a balanced training set with m = 1 is not the optimal solution for our approach.

having an unbalanced training set improves its performance.


parallel sentences as the number of non-parallel sentences increases in the test set.

we see that our neural network based approach obtains better performances by a significant margin.


We notice that a dictionary misleads the decoder when it generates a correct sequence but there is only another form (e.g., plural) of this word, in the dictionary.

the dictionary sometimes helps to get closer to the original word even if this word does not exist in the dictionary and if the training and the test data are from different periods or different languages.


For the four point graded scale, the agreement dropped to 63%.

when binarizing these annotations by combining the first two and the second two classes, an agreement of 84% is obtained.


With a value of 0.21 in terms of Fleiss' κ, the annotator agreement is between slight and fair.

when binarizing the classes as described before, κ becomes 0.36, which corresponds to the respective value of 0.35 reported for our previous clickbait corpus (Potthast et al., 2016).


Two of the 27 publishers, breitbartnews and buzzfeed, do obviously not follow the overall distribution; both send significantly more clickbait than the others.

their contribution to the total amount of clickbaiting tweets (and hence to the corpus' publisher bias) is moderate only.


Considerable effort has been devoted to building commonsense knowledge bases.

they are not available in many languages because the construction of KBs is expensive.


There is a rich body of work on sense embedding, which allows one surface form of a word to have sense-specific vectors (Neelakantan et al., 2014;Iacobacci et al., 2015).

to the best of our knowledge, previous studies in this field do not target sense vectors of concepts for cross-lingual knowledge projection.


We extend the result in the sense that we show the interactions are not only non-linear but also highly complex.

we show that we do not need a non-stationary function to model the relationship (i.e.


The form of the model learned by Kernel Ridge Regression (KRR) is identical to SVR.

kRR uses squared error loss while SVR uses epsilon-insensitive loss.


Second, most features in the feature space are useful.

each of them should have a certain degree of relevance to the task.


So far, linguistic differences have mostly been studied in the context of dialects, usually African-American Vernacular English -AAVE (Jørgensen et al., 2015), using message-level data which offered insight into syntactic (Stewart, 2014) or lexical markers (Blodgett et al., 2016).

not all users from a racial or ethnic group use these markers or, more generally, an associated dialect and usage is different across sociodemographic traits -use of the AAVE is correlated with lower income and education (Rickford, 1999).


The two methods based on the data from (Blodgett et al., 2016) obtain overall good prediction results, with the message-level model surpassing the user-level model in all four cases.

the performance is consistently lower than the best classifier trained on our data set, despite this being an order of magnitude smaller.


Combining surname-based methods with the best text-based model results leads to significantly better results than using text alone, showing that these contain complementary information.

further adding the demographics (age, gender, income, education) as features does not add to the predictive performance, showing that the results are not impacted by any imbalance in the demographic makeup of our data set.


We presented a detailed study of user-level race/ethnicity prediction along the lines of previous work on predicting user traits from text (Burger et al., 2011;Rao et al., 2010;Pennacchiotti and Popescu, 2011;Schwartz et al., 2013;Sap et al., 2014;Volkova et al., 2014;Preoţiuc-Pietro et al., 2015b;Flekova et al., 2016b;Preoţiuc-Pietro et al., 2016;Preoţiuc-Pietro et al., 2017).

with previous research on race/ethnicity, we used labels obtained by directly surveying Twitter users, rather than distantly supervised geo-located data or perceived race labels, which lead to multiple biases and lower accuracies on real data.


the proposed framework also has the model component to discriminate between multiple classes.


Similar to M M F D Concat , in this setting, we keep the automated feature extraction and the fakeness discrimination components.

we replace the interpretable multi-source fusion component by explicitly considering all the sources equally important.


Few would dispute the idiomaticity of "kicking the bucket", as the non-literal meaning in this multi-word expression is largely conventionalized.

in the sentence "One approach would be to draw the line by reference [...]" the expression "draw the line" could be classified as either metaphorical (because it still evokes the literal senses of its constituents) or idiomatic (as it is a fixed expression with lexicalized figurative sense).


Additionally, they make use of concreteness ratings, grounded in the Conceptual Metaphor Theory (Lakoff and Johnson, 1980).

as argued in our introduction, concreteness is also useful for the detection of other kinds of non-literal language.


Their semantic tasks also improve when introducing shortcut connections, i.e., feeding the word representations into all layers of their network.

alonso and Plank (2017) find generally mixed performance of MTL for semantic tasks.


Essentially, this amounts to using one network for multiple tasks, trained jointly.

sLUICE has separate layers for each task, and learns parameters which control the information flow between those task-specific layers.


In other words: both metaphor tasks, where training (and evaluation) data for a particular token or construction is sparse, profit heavily from inclusion of the idiom datasets.

using only the respective other English metaphor dataset does not help.


is wrongly classified as literal by both STL approaches, but correctly labeled as metaphoric by both MTL-all configurations, i.e., with the help of auxiliary tasks.

most of the newly identified metaphors (i.e., found by MTL-all but not by STL) differ between the approaches.


We attribute this to heavy featureengineering on their part, using supersenses and concreteness information.

we perform on par with the state-of-the-art on tok-met (Do Dinh and Gurevych (2016): F 1 = 0.56, our system: F 1 = 0.56).


In this method, a considerably small fraction of tweets, over a time period are collected.

the way in which Twitter makes these set of tweets is unclear, inducing a possible bias.


(Rehbein et al., 2013) proposes an efficient approach to collect German tweets using geolocation features with language filter.


After being introduced, some of them are adopted by other speakers belonging to the same community and possibly spread until they become community norms.

other innovations do not manage to make their way into the community conventions and just disappear after a certain period of time.


Identifying cognates and borrowings by means of computational approaches has attracted considerable attention in recent years (Hall and Klein, 2010;Tsvetkov et al., 2015;Ciobanu and Dinu, 2015).

few studies went beyond this step, and beyond the comparative method, to automate the process of proto-language reconstruction (Oakes, 2000;Bouchard-Côté et al., 2013;.


(2014), as well as the paradigm generalization method to be proposed in section 4, operates on full paradigms, requiring that all of the inflection tables for a given part-of-speech have the same number of MSDs.

this is not the case in the CoNLL-SIGMORPHON shared task data: inflection tables of German nouns, for instance, encompass between four and eight forms.


Among the proposed methods, DAEME performs best in short-text classification tasks, while CAEME is competitive in semantic similarity measurement tasks.

aaEME performs overall well and obtains the best performance in word analogy, relation classification, and psycholinguistic score prediction tasks.


With the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly.

many natural language processing tasks need sense level representation.


With the pre-trained word embedding, some researches propose post-processing models that incorporate with the existing semantic knowledge into the word embedding model (Faruqui et al., 2015;Yu and Dredze, 2014).

word embedding models use only one vector to represent a word, and are problematic in some natural language processing applications that require sense level representation (e.g., word sense disambiguation, semantic relation identification, etc.).


Some researches use zero vector to represent the missing words, whereas some remove those missing words from the dataset.

within this research the reported performance can be compared due to the same missing word processing method and the same similarity computation method.


Since attention largely improves model performance for deterministic Seq2Seq models, it is tempting to include attention in the variational Seq2Seq as well.

our pilot experiment raises the doubt if a traditional attention mechanism, which is deterministic, may bypass the latent space in VED, as illustrated by a graphical model in Figure 1c.


This heuristic is proposed in hopes of better training the variational latent space at the beginning stages.

experiments show that such simple heuristic does not help much, and is worse than the principled variational attention mechanism in terms of all BLEU and diversity metrics.


This is consistent with the evidence that variational latent space may serve as a way of regularization and improves quality .

a small γ a only slightly improves diversity, and hence we did not choose this hyperparameter in Table 2.


However, variational attention outperforms deterministic attention in terms of both quality and diversity, showing that our model is effective in different applications.

we find the improvement is not so large as in the previous experiment.


This latter quantity is bounded by a different range for each measurement feature (see Table 1).

humans may prefer to give measurement values between 0 and 1, where 1 means "this happens as often as possible."


Prior to selection, each candidate measurement must be considered and a model retrained using t sampled candidate measurements.

by applying a number of additional approximations we can run on the weather sentiment dataset from Section 5.4 with over 22,000 candidate measurements.


Structural embeddings are important complements to existing neural architectures.

it is unclear whether they should be supplied as input to the encoder or be left out of the encoding process and directly concatenated with the encoder hidden states.


by the parser may affect the "Struct+2Way+Relation" results.

because the Gigaword dataset does not provide gold-standard annotations for parse trees, we could not easily verify this and will leave it for future work.


It also compares favorably to ground-truth summaries on "fluency" and "faithfulness."

the ground-truth summaries, corresponding to article titles, are judged as less satisfying according to human raters.


We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives.

the adversarially-trained models only produce more types from the head of the vocabulary and not the tail.


We also found that GAN-based systems produce more diverse descriptions than MLE-based systems.

we caution that the GAN-based systems are the only ones in our evaluation that are designed with diversity in mind.


The fourth example shows a slightly challenging case, where both headlines make it difficult to understand the question.

the headline of our method included the term "winning bidder", so at least the answerer can assume that the question is about some sort of auction trouble.


(2015) proposed a neural headline generation model, there have been many studies on the same headline generation task (Takase et al., 2016;Chopra et al., 2016;Kiyono et al., 2017;Ayana et al., 2017;Raffel et al., 2017).

all of them are abstractive methods that can yield erroneous output, and the training for them requires a lot of paired data, i.e., news articles and headlines.


Furthermore, recent sophisticated neural models for extractive summarization (Cheng and Lapata, 2016;Nallapati et al., 2017) basically require large-scale paired data (e.g., article-headline) to automatically label candidates, as manual annotation is very costly.

such paired data do not always exist for real applications, as in our task described in Section 1.


Unlike the above tasks, SCT (Mostafazadeh et al., 2016) provides large-scale supervised training stories of temporal and causal relations, ensuring a high-quality evaluation for common sense knowledge understanding of mechanisms.

the published ROCStories could not be used directly in supervised learning.


Although these models generate better performance, the characteristics of question is still ignored.

research about visual question generation is much less (Ren et al., 2015;Vijayakumar et al., 2018;Mostafazadeh et al., 2016;Shijie et al., 2017).


Their method performs word sense discrimination and embedding learning by using a nonparametric estimate of the number of senses per word type.

our model does not assume that there is a finite set of discrete senses per word.


Meanwhile, a number of deep learning models are designed to take up the challenges, most of which focus on attention mechanism Seo et al., 2017;Cui et al., 2017a;Kadlec et al., 2016;.

how to represent word in an effective way remains an open problem for diverse natural language processing tasks, including machine reading comprehension for different languages.


Intuitively, word-level representation is good at catching global context and dependency relationships between words, while character embedding helps for dealing with rare word representation.

the minimal meaningful unit below word usually is not character, which motivates researchers to explore the potential unit (subword) between character and word to model sub-word morphologies or lexical semantics.


The results indicate that for a task like reading comprehension the subwords, being a highly flexible grained representation between character and word, tends to be more like characters instead of words.

when the subwords completely fall into characters, the model performs the worst.


Not only for machine reading comprehension tasks, character embedding has also benefit other natural language process tasks, such as word segmentation , machine translation (Luong and Manning, 2016), tagging (Yang et al., 2016; and language modeling (Verwimp et al., 2017;Miyamoto and Cho, 2016).

character embedding only shows marginal improvement due to a lack internal semantics.


proposes multi-dimensional/vectorial self-attention pooling on the top of self-attention network instead of BiLSTM.

both of them didn't consider multi-head self-attention.


There is no consensus on which set of labels should be uniformly used.

paraphrasing involves rewriting the noun compound as a paraphrase which conveys its meaning explicitly, e.g., orange juice: "juice made from orange" or "juice with orange flavour".


The advantage of prepositional paraphrasing as compared to labelling is that the set of prepositions is finite, limited and pre-defined.

the shortcoming is that the information is too coarse-grained for downstream tasks.


Among statistical approaches, supervised approaches rely on annotated data that needs to be sufficiently large and representative enough of the underlying problem.

such datasets are rare, and the ones that do exist are small and heavily skewed, which makes the learning more difficult.


The correct paraphrase of this noun compound is 'tree of apple'.

the system considers the paraphrase 'tree with apple' to be correct.


Such observations indicate the impact of discourse features.

sometimes contextual cues from the previous comments are not enough and misclassifications are observed due to lack of necessary commonsense and background knowledge about the topic of discussion.


The use of the words doctor, patient, and bronchial in the setup lead the listener to assume that the man is seeking medical advice.

the punchline, the doctor's wife's response along with her description, reveals the man's true intent.


Extended Lesk (Banerjee and Pedersen, 2003), for example, compute the relatedness between two words as a function of the size of the overlap of their glosses.

such measures do not fully address the short comings of word embedding similarities.


Any measure based on a distributional semantic approach, including both Extended Lesk and Word2Vec, at its core relies on word co-occurrence to extract relationships.

not all relationships are evidenced by co-occurrence.


By default, word association and Word2Vec features are computed across all word pairs in a document.

we also experiment computing these features only across pairs of humour anchor (HA) words.


Also the overall performance of this model is low and according to the FNC metric, TalosTree would even outperform TalosComb.

talostree returns almost no predictions for the DSG class, although it performs exceptionally well in terms of FNC.


Lexical cue words, such as reports, said, false, hoax play an important role in classification.

the systems fail when semantic relations between words need to be taken into account, complex negation instances are encountered, or the understanding of propositional content in general is required.


The features that have best positive correlations are the phrase ratio of verb phrases (PR VP) and the number of subordinating conjunctions (SBAR Count).

the features that have consistent negative correlations are the length ratio (LR NP) and the average length of noun phrases (AP L 2 NP).


Existing approaches in spam review detection mainly focused on exacting linguistic features and behavioral features.

linguistic features are ineffective when they are used to detect the real-life fake reviews (Mukherjee et al., 2013b;Wang et al., 2017b), and it usually requires a large number of samples to make the observations on behavior features.


Results shows that their model RE* benefits a lot from unlabeled data.

this is at the expense of efficiency since the training set size increases dramatically when the unlabeled data are added.


On one hand, our AE model trained on the small labeled data significantly outperforms the RE* model trained on the whole dataset, let alone our enhanced AE* model.

when RE* is reduced to RE, its performance drops a lot.


This suggests that the performance of RE* is highly dependent on the size of training data.

our model is less sensitive to the training size than RE.


These semi-supervised naive Bayes algorithms can be directly applied to text classification applications with a few labeled documents.

manually collecting a small number of labeled documents remains expensive in many text analysis applications.


For each algorithm, we independently run it 10 times, and show the average results in Tables 2 and 3.

comparison of dataless algorithms to SNB, we can observe that the proposed PL-DNB algorithm performs better in all settings.


In terms of Reuters, PL-DNB performs relatively stable as δ ∈ [0.1, 0.6], but worse when δ becomes larger than 0.6.

the performance of Newsgroup gets stable before δ achieves 0.9, especially when the set of S D is utilized.


As such questions might appear in one language but not in another, it is much tougher to exploit questions across languages.

striking features of an image are usually picked up by several questions.


Similarly, cognitive research on fluency showed that people rate stimuli that are processed more easily higher (Belke et al., 2010).

many modern artists like Picasso or Schönberg complicated the processability of their works using processes of abstraction in order to prevent such automated or fluid forms of art comprehensibility.


For most QA systems, raw text and structured knowledge graph are used as their knowledge.

raw text corpora are hard to understand for machine.


Jauhar (2017) also evaluates his model on Elementary School Science Questions (ESSQ) dataset.

the structure of those two datasets are quite different.


In this case, entire books, both in the training set and testing set, will be processed.

chapter lengths vary significantly from 158 terms up to 169,588 terms, and we must select a fixed length for each (given the nature of the applied models).


Additionally, this research has shown that across the corpus, there was a 6% gain in accuracy when training and evaluating on the first 5,000 words of each book over training and evaluating on the last 5,000.

there was an additional 2% increase in accuracy when training and evaluating on the random 5,000 word subtexts extracted from each book.


For MIT Restaurant, MIT Movie, CADEC, i2b2 2014 and i2b2 2006 (Figure 2), the neural network approaches have better recall than PRED and PRED-CCA across all dataset sizes; this results in the neural network approaches having a greater F1 score for these datasets in most cases (the exceptions being MIT Restaurant with under 500 sentences, and CADEC with 500 sentences).

for GUM, re3d, NIST IE-ER, MUC 6 ( Figure 4) and Ritter (Figure 3), we observe that initially PRED and PRED-CCA have higher recall than the neural network approaches, but the neural networks' recall surpasses them once the target domain dataset is large enough.


But, unless we know where "Ganapathy Colony" is, the water level data cannot enhance situational awareness and inform disaster response applications such as storm surge modeling or forecasting.

pragmatic influences on writing style shorten names to reduce redundant content in social media.


Representing a question as a bag of words might be too simple.


The true answer path should be "Joseph P Kennedy Sr − −−−−−−−−− →New York County".

the middle entity decided by IRN is "Rosemary Kennedy" who is also a child of "Joseph P Kennedy Sr", but her death is not included in KB.


In traditional evaluations such as word similarity and word analogy, the aforementioned context-aware word embeddings work well since semantic information plays a vital role in these tasks, and this information is naturally addressed by word contexts.

in real-world applications, such as text classification and information retrieval, word contexts alone are insufficient to achieve success in the absence of task-specific features.


Music structure discovery is a research field in Music Information Retrieval where the goal is to automatically estimate the temporal structure of a music track by analyzing the characteristics of its audio signal over time.

only a few works have addressed such task lyrics-wise Watanabe et al., 2016;Baratè et al., 2013).


The SSM phon exhibits a small but measurable performance decrease from SSM string , possibly due to phonetic features capturing similar regularities, while also depending on the quality of preprocessing tools and the rulebased phonetic algorithm being relevant for our song-based dataset.

despite lower individual performance, SSMs are still able to complement each other with the SSM all model yielding the best performance.


In particular, feed-forward CNNs with word embedding have been proven to be a relatively simple yet powerful kind of models for text classification (Kim, 2014).

the reliance on large-scale corpus has been a formidable constraint for deep neural networks (DNNs) based methods due to their numerous parameters.


The units tend to borrow more features from other tasks will more frequently activate the leaky gate.

the update gate will be more active to preserve more information in current task.


Previous works focused on detecting claims in a small set of documents or within documents enriched with argumentative content.

pinpointing relevant claims in massive unstructured corpora, received little attention.


Relying on this formulation led to promising precision results in the challenging task of corpus wide claim detection, albeit with low recall .

specifically, while each of the sentences in Table 2 contains a valid and relevant claim, only s1 satisfies their query; s2 satisfies only the first part of the query ('that' preceding the MC); s3 satisfies the second part of the query (CL token following the MC); and s4 only mentions the MC.


We test the performance of these DNNs over a distinct test set of 50 topics, also from (Levy et al., 2017).

in contrast to this previous work, we consider a much more relaxed query that only requires the MC to be mentioned in the sentence.


The first study focusing on morphologically rich languages to employ neural networks (Demir andÖzgür, 2014) contains a regularized averaged perceptron (Freund and Schapire, 1999) and relies on handcrafted rules along with pretrained word embeddings.

they refrain from using output from external morphological disambiguators and only rely on the first and last few characters of a word as features.


We see that the JOINT2 model is performing better than just calculating two losses at the last layer as we did in the JOINT1 model.

applying the Welch's t-test between the JOINT1 and JOINT2 runs does not strongly imply this difference (p = .24).


(2017) mapped the dataset from Freebase to Wikidata.

2 our migrated SIMPLE-DBPEDIAQA dataset has roughly twice the number of mapped questions.


Next datasets with comparably high performance measures are Blogs and DailyDialogs.

crowdFlower and Electoral-Tweets seem to be the most challenging in the within-corpus setting.


In terms of annotation procedures, these experiments allow almost for no judgement, since most of the datasets use expert annotation and we only have few examples for the other two ways of annotation (crowdsourcing and distant supervision) being used.

we could observe that the crowdsourced datasets are more difficult which might be due to a more noisy annotation.


Because of its relatively low scores, we don't experiment with more complex generative models.

a simple discriminative classifier such as Logisitic Regression performs significantly better than Naïve Bayes on the random version for all the datasets.


When this condition is satisfied, the implication is that the system ranks the positive sentence below the negative sentence, or does not sufficiently rank the positive answer above the negative answer.

if the correct sentence has a score higher than the incorrect sentence by at least a margin m (i.e., h , and then the above expression has zero loss.


In other words, MRR is the average of the reciprocal ranks of results for the questions in Q.

if the set of correct candidate answers for a question q j ∈ Q is {d 1 , d 2 , ..., d m j } and R jk is the set of ranked retrieval results from the top result until you get to the answer d k , then MAP is calculated as follows: When a relevant answer is not retrieved at all for a question, the precision value for that question in the above equation is taken to be 0.


The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity.

this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models.


Most previous studies on NER focus on a certain set of predefined NER types, such as organization, location, person, date, and so on, where a certain amount of labeled data is provided to train the models.

different applications require particular entity types, such as "Brand" and "Product" in Ecommerce domain, and "Company" for finance industry.


that is regarded as a positive instance with two "Product" names are correctly matched by the distant supervision method.

in practice we find that the automatically labeled NER data suffers from two problems, i.e., incomplete annotation and noisy annotation, which negatively affect the performance of NER systems.


In order to overcome the challenge of data deficient, some approaches based on weakly supervised learning (Nadeau et al., 2006;Riloff and Jones, 1999) have been proposed and successfully expand training data and feature space.

it is difficult to implement these methods on Chinese tasks because of the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation, and it may cause large number of matching errors.


To learn and tune the parameters of the local models for CoNLL and TAC we use their own training and development splits.

to learn and tune the parameters of the global model (the discrepancy propagator, the heuristic and pruning functions) we only use the CoNLL training and dev-sets.


SuperAgent considers both QA collections and reviews when answering questions.

there is no mutual coordination for the response generation from different information sources, and the response from SuperAgent cannot be presented as a ranked list of snippets.


Many sentiment analysis systems rely primarily on the sentiment of individual words.

precise sentiment analysis often requires lexical-semantic knowledge that goes beyond word-level sentiment, even when dealing with short phrases such as bigrams.


Other approaches aim to learn sentiment composition from sentiment-labeled texts.

sentiment-labeled texts might not be available for certain domains or languages.


to previous methods, our method does not require sentiment-labeled texts or feature engineering.


The above previous work focused on learning sentiment of unigrams, and optionally of multi-word expressions (conflated into single tokens), and used word2vec embeddings (Mikolov et al., 2013).

we are interested in learning sentiment composition from bigrams, and therefore we are mostly interested in compositional bigrams, and aim to construct a sentiment lexicon that contains hundreds of thousands of them.


Let C(c, w) be the set of bigrams in the bigram lexicon that satisfy the condition of class c for word w, and let S(c) be the set of bigrams whose polarity is the one predicted by c. The precision of w with respect to c can be defined as: The above formula gives uniform weights for all the bigrams in C(c, w).

we found that it is beneficial to also take into account the uncertainty stemming from the automatic sentiment prediction of the unigrams and bigrams.


This is in line with our hypothesis that explicit modeling of morphology may capture interactions of elements in the sequence and allow for better generalization thereof.

with MLPs and CNNs, RNNs consider the entire sequence for classification, and so it may be the case that previously unseen tokens in the sequence may undermine classification in token-based settings.


Classical architectures include convolutional neural networks (CNNs) (Zeng et al., 2014), recurrent neural networks (RNNs) (Xu et al., 2015), etc.

above methods are still insufficient for domain knowledge acquisition due to two challenges: (i) most domain entities rarely occur in the corpus, hence pattern-based methods easily suffer from the feature sparsity problem; (ii) a domain knowledge graph tends to be incomplete w.r.t.


Therefore, considering the sparsity of domain knowledge, these methods applied on such corpus will introduce plenty of small classes, which are in fact noises and thus meaningless.

our ssCRP-based approach works more effectively since we only discover one large class with confident instances in each iteration.


Consequently, the attention of the models to such words would lead to a correct prediction in this case.

for the positive sentences, it might be the case that the words along the dependency paths help to include some words that are crucial to police killing prediction, but do not appear in the semantical word lists.


Sentences often have a long list of comma separated conjuncts and not separating all of them would mean a substantial performance loss for end-tasks like Open IE.

our approach eschews the first principle, as we find that it is not true often enough to be helpful.


Most language models approximate this via an n-gram probability for a fixed context window of length n − 1, instead of taking the entire sentence so far.

using the language model score is ineffective for two reasons.


Previous work has given credit to a system only when both boundaries of a conjunct match exactly (Ficler and Goldberg, 2016).

this is not ideal for downstream tasks like Open IE.


OLLIE follows the idea of bootstrap learning of patterns based on dependency parse paths.

while WOE relies on Wikipedia-based bootstrapping, OLLIE applies a set of high precision seed tuples from its predecessor system REVERB to bootstrap a large training set.


Considering recall, Graphene (27.2%) is able to compete with other high-precision systems, such as PropS (26.7%).

it does not reach the recall rate of ClausIE (33.0%) or OpenIE-4 (32.5%).


The recent success of deep learning for NLP tasks is at least partially due to the fact that such models are exposed to large quantities of labeled data.

obtaining such rich labeled data is a costly proposition and seldom feasible in many realistic scenarios.


Note, however, that most of these works that customize embeddings for a specific task rely on some form of supervision.

our approach that learns a custom representation for the NEC task is lightly supervised, with a only few seed examples per category (Section 4).


This is analogous to recent work (Jia and Liang, 2017;Burlot and Yvon, 2017) on developing automated adversarial evaluation schemes for reading comprehension and machine translation.

unlike these efforts, our stress tests allow us to study model performance on a range of linguistic phenomena.


Further advances with Neural Networks (NNs) have once more motivated efforts to develop a large natural language inference dataset, SNLI (Bowman et al., 2015), since NNs need to be trained on big data.

meaning is not something we obtain just from text and the ability to reason is not unimodal either.


The present paper presented a first step in this direction using a version of an existing TE dataset which was augmented with images that could be paired directly with the premises, since these were originally captions for those images.

it is important to note that in this dataset premise-hypotheses pairs were not generated directly with reference to the images themselves.


(2015) extend one-hop reasoning regimes such as TransE (Bordes et al., 2013) to multi-hop PQA.

these basic one-hop models do not encode the relation order when used in compositional training schemes.


Apart from these baselines, it is also feasible to compare with the baselines based on composition of one-hop triple-based embedding models.

the performance of these baselines is very poor for this task (Neelakantan et al., 2015) and therefore, we do not include them in our comparison.


We expected that H@10 should decrease gradually.

figure 4 shows that even-length paths are harder than odd-length ones.


Decision-level fusion is a commonly used strategy for fusing heterogeneous inputs, combining the independent modality outputs by using several specific rules.

the lack of mutual association learning across modalities is a major limitation of applying decision-level fusion (Zhang et al., 2017).


TTR becomes unstable in learner English because of spelling errors.

this is not the case with K. In the next section, we explore the results theoretically and empirically to deepen our understanding of these phenomena.


Suppose that a spelling error was created from w ∈ W and that 100r% of all occurrences of w underwent the noise and were replaced with it.

then, the contribution of w to the entire value of K decreases by: the newly created spelling increases the value of K by: In total, the difference caused by a spelling error can generally be written as: Accordingly, it follows that the influence is only dependent on r. the difference becomes a maximum when r = 1 2 .


To be precise, we have revealed that the difference in its value caused by spelling errors is relatively high throughout the tree groups, observing not less than 16% difference.

this is not the case with K, which shows no more than 1% difference throughout the three groups.


Note that, different from H&N14, which only contains favor and against posts, SemEval16 dataset contains none posts, which do not express any stance.

the final metrics disregard the None class.


Comparison between "-Hyper" and "-Hyper, -Ling", we can find that linguistic attentions with each linguistic features does have significant improvement.

using LSTM to detect stance (LSTM) does not obtain a better performance compared to concatenating linguistic information directly ("-Hyper, -Ling").


grammar rules, so it is relatively easy to grasp some patterns of correction.

in the HSK dynamic composition corpus built by Beijing Language and Culture University, which is the largest available Chinese learner corpus at the time of this study, WUE is the most frequent lexical-level error.


The accuracy is less than 70% and MRR is less than 80%.

for closed-set word types such as prepositions (P), our system performs very well, reaching accuracy 0.81 and MRR 0.88.


Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity.

useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do not accord with these assumptions.


Distributional representations of concepts are often easy to obtain from unstructured data sets, but they tend to provide only a blurry picture of the relationships that exist between concepts.

knowledge graphs directly encode this relational information, but it can be difficult to summarize the graph structure in a single representation for each entity.


This modular approach conveniently separates the distributional data and entity representation learning from the knowledge graph and retrofitting model, allowing one to flexibly combine, reuse, and adapt existing representations to new tasks.


Synonym is mostly defined as "a same-language equivalent" (Adamska-Sałaciak, 2010; Adamska-Sałaciak, 2013) and "does not exceed the limits of a single language" (Gouws, 2013), while for bilingual contexts the term translational equivalent is used.

(Martin, 1960;Klégr, 2004;Hahn et al., 2005;Hayashi, 2012;Haiyan, 2015;Dinu et al., 2015) recognize interlingual synonymy and use either the term foreignlanguage equivalent, cross-lingual synonym, synonymous translation equivalent or bilingual synonym.


Furthermore, we analyzed the advantages and disadvantages of our model, and found that it is better at capturing semantic similarity of two sentences than averaging models, especially when they have little word overlap but similar meanings.

it tends to overestimate the low semantic similarity of a sentence pair.


Traditional Korean morphological analysis algorithms operate at the Eojeol level and yield all ambiguous parses (Table 1) that lead to that particular Eojeol, including the morpheme transformations and tags.

the model 1 proposed in this paper receives input at the sentence level and attempts to produce the one correct sequence of transformations and tags for all Eojeol within the sentence according to the context (Table 2).


Morphological analysis of the Korean language has traditionally been performed in several ways, including separation of Korean characters into graphemes by using linguistic knowledge, lattice tree lookup (Park et al., 2010), application of regular and irregular inflection rules (Kang and Kim, 1992), morphosyntactic rule sets, and by using a pre-computed dictionary (Shim and Yang, 2004).

we investigate whether morphological analysis of Korean is feasible without the use of any of these techniques and without a dictionary by making the assumption that common transformations and their underlying grapheme modifications can be easily recognized and learned with a Bi-LSTM model.


It has been used for event detection (Pozdnoukhov and Kaiser, 2011;Ertl et al., 2012;Vavliakis et al., 2012;Lau et al., 2012;Zhou and Chen, 2014), summarization, or finding influential users in social media.

only few studies used LDA to detect topic changes over time (Lau et al., 2012;Zhou and Chen, 2014).


The evolution of topics in on-line LDA models is usually shown using the most probable words from the word-topic distributions (Hoffman et al., 2010;Lau et al., 2012;Zhai and Boyd-Graber, 2013).

the same top words can appear in different topics, making differences between topics hard to show.


Cross-lingual embedding and dictionary classifiers provide a stronger baseline than LP, outperforming SVM data+resource when training data is sparse.

adding them as features to the SVM results in a classifier that consistently improves upon all other systems, even at small training sizes of only 20%.


The dictionary mapping approach ( §4.2.1) has been shown to be a strong stand-alone classifier and SVM feature (Table 5), slightly outperforming the cross-lingual word embedding approach ( §4.2.2).

the underlying English-German dictionary by DictCC is of considerable size, consisting of over 1.1 million translation pairs.


Stress Marking In the UoI corpus, all words with two or more syllables have a diacritic mark to indicate the location of stress.

the resources that we collected are not consistent in the use of such a diacritic.


(He and Lin, 2016;Wang et al., 2017;Wang and Jiang, 2016;Trischler et al., 2016;Parikn et al., 2016.).

all above approaches are similar to our One vs. One Matching model which deals with the matching measurement between one sentence (or one piece of text) and another sentence (or another piece of text).


However, all above approaches are similar to our One vs. One Matching model which deals with the matching measurement between one sentence (or one piece of text) and another sentence (or another piece of text).


Distributed representations of words play a major role in the field of natural language processing by encoding semantic and syntactic information of words.

most existing works on learning word representations typically regard words as individual atomic units and thus are blind to subword information in words.


That network shares a similar architecture with ours.

our architecture is different from the early network (Kim et al., 2016) in that ours is designed with a simpler network architecture and a different output layer based on the pre-trained word embeddings.


Synonyms are proposed for both words and word senses.

it turns out that not all synonyms for a given word are grouped together in distinct senses.


This is the hypothesis that describes the similarity between two words as the similarity of their closest senses (Budanitsky and Hirst, 2006).

if the synonym is monosemous, the algorithm 1 is applied (we note this variant algorithm 2).


(2013) observe that 25% of the MWEs in their test corpus are unseen in the training data, and that at most 19% of them could be correctly identified by their system based on sequence labeling.

the authors do not specify how unseen MWEs are defined, that is, if variants are counted as seen or unseen.


This feature identifies the similarity between play a.DET very.ADV important.ADJ role and play his.DET first.ADJ major role.

linear similarity does not capture postnominal adjectives (e.g.


For instance, the noun in many VMWEs must remain in singular, as in (8) vs. (10), or in plural, as in (11).

cOMP features might fail in case of rare VMWEs, and cannot be calculated for hapaxes.


Another common choice for parallel corpus in multilingual research, the Bible, is available in 2,530 languages (Agić et al., 2015).

2 studies show that its archaic themes and small corpus size (1,189 chapters) can limit performance (Hao et al., 2018;Moritz and Büchler, 2017).


Another line of research focuses on using multilingual dictionaries as supervision (Ma and Nasukawa, 2017;Gutiérrez et al., 2016;Liu et al., 2015;Jagarlamudi and Daumé III, 2010;Boyd-Graber and Blei, 2009).

to parallel corpora, dictionaries are widely available and often easy to obtain.


The fewer entries the dictionary provides, the more VOCLINK degrades to monolingual LDA.

sOFTLINK can potentially transfer knowledge from the whole corpus.


Finding this definition automatically is not as trivial as it might sound and there is a lot of literature on this topic.

a lot of cases remain where we cannot find such a definition, because the extraction method fails or because there is no definition in the paper.


For most Indigenous languages, learning morphology automatically from corpora is not a viable option.

symbolic systems, especially those based on finite-state transducers (FSTs) have been successfully implemented for a number of languages.


The feasibility of implementing augmented and virtual reality projects is aided by the widespread interest in the technology and 3D game engines like Unity and Unreal.

there are still very few implementations for Indigenous languages in Canada.


Nonetheless, the noted grammatical similarities among these languages are expected to be sufficient for the generalizability of the pluralization approach.

meinhof's NC definition (Table 3) has several limitations when applied to computational tasks, which impede pluralization and generalizability: 1.


We further found that, similar to isiZulu and Runyankore, phonological conditioning was also required in chiShona, isiXhosa, Kinyarwanda, and Luganda; only Kikuyu does not require phonological conditioning.

as explained in Section 2, the rules for phonological conditioning are languagespecific.


Similar to semantic networks, we use a graph based representation in which nodes are associated with words.

to semantic networks, however, these edges are labelled with a vector, meaning that relation types are modeled in a continuous space.


Accordingly, it was found in (Vylomova et al., 2016) that a relation classifier which is trained on word vector differences is prone to predicting many false positives.

we can expect that our relation vectors are modeling relations in a far less ambiguous way.


In contrast, we can expect that our relation vectors are modeling relations in a far less ambiguous way.

these relation vectors are limited to word pairs that co-occur sufficiently frequently.


In particular, the diffvec vectors all express relationships from the metalwork domain (e.g., 'heavymetals' or 'annihilator-metal'), which reflects the fact that the music-related interpretation of the word 'metal' is not its dominant sense.

since our relation vectors are exclusively learned from sentences where both words co-occur ('heavy' and 'metal' in this example), the vector for 'heavy metal' clearly captures the musical sense (see e.g., 'thrash-metal' or 'glam-metal' in the original space).


The averaging model clustered questions about islands; we observed similar behavior using weighted averaging, doc2vec and GRAN.

skip-th clustered questions that start with "what country", which happens to be more suitable for identifying the LOC question type.


In this approach, frequently co-occurring word sequences are considered FEs.

noise such as 'is one of the' cannot be removed.


Domain-specific technical terms, such as natural language processing or reactive oxygen species, are unlikely to be extracted using the proposed method.

the usage of FEs is shown to differ across domains, which implies that the expressions should be re-ranked according to the users' discipline when candidate expressions are presented to users of the writing assistance system.


Ideally, there would be a single intrinsic metric for identifying "good" embeddings -and there are many proposals for such a metric (including word relatedness and analogies).

none of them have been shown to predict performance on a wide range of tasks, and there is evidence to the contrary (Chiu et al., 2016).


There are multiple proposals for "subconscious intrinsic evaluation" (Bakarov, 2018) based on correlations with psycholinguistic data such as N400 effect (Van Petten, 2014;, fMRI scans (Devereux et al., 2010;Søgaard, 2016), eye-tracking (Klerke et al., 2015;Søgaard, 2016), and semantic priming data (Lund et al., 1995;Lund and Burgess, 1996;Jones et al., 2006;Lapesa and Evert, 2013;Ettinger and Linzen, 2016;Auguste et al., 2017).

there are no large-scale studies that would show the utility of these methods in predicting downstream task performance.


The idea behind the word analogy task (Mikolov et al., 2013b) is that the "best" word embedding is the one that encodes linguistic relations in the most regular way: simple vector offset should be sufficient to capture semantic shifts such as F rance : P aris to Japan : T okyo.

this view of linguistic relations (and analogical reasoning) is oversimplified, and performance on word analogies has also been shown to depend on cosine similarity between source word vectors Linzen, 2016;Levy and Goldberg, 2014b).


Crucially, all these approaches make the same core assumption: that there is one feature of a representation that would make it the "best" (the highest correlation with human judgements, the most regular vector offsets, the closest approximation of a linguistic resource, etc.)

language is a multifaceted phenomenon, and different NLP tasks may rely on its different aspects -which would doom any one-metric-to-rule-them-all approach.


One more important observation from this experiment is that all the extrinsic and intrinsic tasks have high correlations with more than one LD factor, which illustrates the point about tasks being complex ensembles of various linguistic features.

it is only by breaking them down into smaller, controllable factors that we can explain and improve on them.


However, constructing training corpus for all languages and words is tremendously expensive, so the supervised approaches generally have some limitations on the set of the words that can be disambiguated.

the knowledge-based unsupervised approaches utilize lexical knowledge bases (LKBs) such as a Wordnet (Banerjee and Pederson., 2003;Chaplot et al., 2015).


However, in terms of the macro average score of SemEval-2013 and SemEval-2015, Wordsim_iterSRP2vSim shows higher performance than the Moro 14.

s, unsupervised knowledge-based approaches, including our system, generally has poorer performance than supervised approaches in the SemEval-2015 dataset.


This method has been proposed to solve the computational complexity problem of finding the optimal combination among all possible set of senses.

due to the nature of the greedy search, sometimes it makes hard to inference correct sense of the ambiguous word because of the error propagation from a previous result can determined answer.


(2015) empirically found that Expected Wins performs better than Trueskill.

almost all subsequent work ignored this finding and used the Trueskill model instead.


In the HTies variant, M 2 achieves statistically significant improvements compared to the other two metrics in both expanded and unexpanded sets.

for NoTies variant, GLEU achieves the best result.


In the case of its counterpart BLEU, if some n-grams of the hypothesis and reference match, it rightly assigns a non-zero score as the system shows some ability to perform translation.

for GEC, simply copying the input can result in matching several n-grams from the reference despite the system showing zero ability to perform correction.


It can be argued that GLEU intends to reward GEC systems for detecting errors by assigning a partial credit to systems that make spurious changes at locations where corrections are deemed necessary by human annotators.

this will encourage building GEC systems that provide inaccurate feedback and potentially mislead the end users (primarily language learners).


In Example 2, when multiple references are used, GLEU gives a higher score to Hypothesis 3 (ungrammatical) than Hypothesis 1 and 2, both of which are grammatical and each matches one of the two references exactly.

both M 2 and I-measure assign a lower score to Hypothesis 3 and conform to our intuition.


Nonlinear Affine Transformation Usually, a BiLSTM decoder takes the concatenation g i of the hidden state vectors as output for each hidden state.

in the SRL context, the encoder is supposed to distinguish the currently considered predicate from its candidate arguments.


Mapping a word into two different vectors can help the model disambiguate the role that it plays in different context.

biaffine Scoring In the standard NMT context, given a target recurrent output vector h  considering that in a traditional classification task, the distribution of classes is often uneven, and that the output layer of the model normally includes a bias term designed to capture the prior probability P (y i = c) of each class, with the rest of the model focusing on learning the likelihood of each class given the data P (y i = c|x i ), (Dozat and Manning, 2017) introduced the bias terms into the bilinear attention to address such uneven problem, resulting in a biaffine transformation.


The existing positional encodings are mostly fixed encoding like sinusoid type or learned positionwise encoding which is set up per each word independently.

light-house positional encoding defines only one distance embedding with respect to one time step between words.


In particular, as the difficulty of domains associated with 'open-vocabulary' slots increased, like calendar and message, a larger gap in performance emerged to the amount of about a 5% absolute gain for slot filling and about a 1% error rate for intent detection.

multi-task learning with the intent detection model did not have much effect on the performance of slot filling.


Conventionally, grammatical words, especially function words, have been proposed for stylometric authorship attribution since they are independent of content.

character n-gram based approaches have largely outperformed function word based approaches (Kestemont, 2014) indicating that some lexical words may also help with authorship attribution.


It is known that function words are independent of content and are useful for representing style.

the success of character n-gram approaches in authorship attribution indicate that some lexical words may also be useful for authorship attribution.


For single-domain IMDB1M dataset, it can be observed from Table 3 that excluding all common nouns (no N N ) and proper nouns (no N N P ) affects the attribution performance drastically.

masking only topic words corresponding to common nouns (no topic N N ) and proper nouns (no topic N N P ) seems to improve attribution performance compared to masking them completely (no N N P , no N N ).


This could possibly be due to the heavy dependence on common nouns and proper nouns for attribution under those scenarios.

for cross-domain Guardian10 dataset, it can be observed from Table 4 that excluding proper nouns (no N N P ) completely yields a marked improvement in performance for both crosstopic and cross-genre experiments.


We evaluated the role of syntax using a purely syntactic language model and show that syntax may be useful with cross-genre attribution while cross-topic attribution and single-domain attribution may be benefit from both syntax and lexical information.

syntactic language models are not discriminative by themselves and need to be used in conjunction with more successful character n-gram models.


For common nouns, verbs, adjectives and adverbs, masking off certain topic words yield better performance suggesting that the remaining words corresponding to these lexical POS may help represent style.

proper nouns seem to be heavily influenced by topic and cross-domain attribution may benefit from completely masking them.


On the one hand, we must encode firm-specific information into the dense document representations so as to make them different across targets.

we must identify the most informative sentences while disregarding noise for the prediction.


Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents).

most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics.


The underlying intuition known as distributional hypothesis (Harris, 1954) can be summarized as: "a word is characterized by the company it keeps" (Firth, 1957).

there is a lack of theory to justify why it works, even for a simple task such as word classification or clustering.


Besides, although the results of SSA are different the two corpora share again the same tendency: they both reach their best when using DSC without WMI.

dSC always improves the results whether it is applied alone or combined with WMI.


These works show that a strong relationship exists between computational semantic models and neural representations.

it remains to be seen how cognitive semantic representations, including localized neural activation patterns can help improve the performance of computational semantic models, especially for complicated classification and recognition tasks.


Word prediction models tend to perform better in natural language processing tasks such as analogy, similarity, synonym detection, concept taxonomy  and sentiment analysis (Socher et al., 2011;Socher et al., 2013).

their relationship with cognitive lexical representation is not yet well understood, at least to a degree that would allow us to improve current computation lexical semantic models.


On the other hand, the random baseline system creates the most diverse tag space by using all of the possible tags.

its lower micro-F1 score of 6.30% makes it impractical to be used in real world scenario.


Similar work has, to the best of our knowledge, only been done in the psychology domain.

related work from this area does not target the goal of predictive modeling (Stevenson et al., 2007;Pinheiro et al., 2017).


Thus, an important difference between SHR and ISR is that the former is computed on a single data set whereas the latter requires two different data sets with overlapping items.

iSR can be computed on the final ratings alone, whereas SHR requires knowledge of the judgments of the individual raters.


It is important to note that the decision for N * = 20 is necessarily arbitrary, to some degree, with higher SHR estimates arising from higher values of N * .

20 raters are often used in psychological studies (Warriner et al., 2013;Stadthagen-Gonzalez et al., 2017b), while being way higher than the number of raters typically used in NLP for emotion annotation, both for the word and sentence level (Yu et al., 2016a;Strapparava and Mihalcea, 2007).


Previous work has limited itself to data sets comprising all three VAD dimensions with the implicit belief that Dominance provides valuable affective information which is important for ERM.

since only about half of the data sets developed in psychology labs (and even less provided by NLP groups) actually do comprise Dominance, this decision massively decreases the amount of data sets at hand.


In addition, observing the confusion matrix in JMTE suggests that in both tasks data points are separable, since misclassified data points are not skewed towards particular or the most popular classes.

emotion tag surprise is mainly misclassified with joy and discussed, we further observed the data points to address this issue and noticed most of these data points imply surprise and even as human it was difficult for us to categorize them as surprise.


They found that the results of their functional approach suggests (in their own language): "Negative emotions are many and specific.

positive emotions are few and less specific."


Other frameworks include ParGram (Butt et al., 2002), the MetaGrammar project (de La Clergerie, 2005) and Grammatical Framework (Ranta, 2004).

the Grammar Matrix provides a particularly suitable framework for our analyses because it requires its libraries to be typologically robust.


If the modifier attaches to a sentence, the matrix clause must already have a subject, signified by an empty subject list ([SUBJ ]).

if it attaches to a verb phrase, we constrain this list to be non-empty ([SUBJ [ ] ]) and if it can attach to either the VP or S, we leave this constraint underspecified.


This results in three sentences failing to parse.

upon constructing such a rule, we confirmed that if this rule were in the grammar, those sentences would parse correctly.


Recurrent neural networks have achieved great success in many NLP tasks.

they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs.


To solve this problem, several scholars try to use convolutional neural networks (CNNs) (Lecun et al., 1998) instead of RNNs in the field of NLP (Kim, 2014;Kalchbrenner et al., 2014;Gehring et al., 2017).

cNNs may not obtain the order information of the sequences, which is very important in NLP tasks.


Most of the researches get faster by improving the recurrent units.

the traditional connection structure has scarcely been questioned, in which each step is connected to its previous step.


Firstly, with only a few exceptions, much existing work focuses on "pairwise" MTL where there is a target task and one or several (carefully) selected auxiliary tasks.

can jointly learning many tasks benefit all of them together?


Moreover, at least one task significantly contributes to the success of All MTL at some point; if we remove it, the performance will drop.

cOM generally negatively affects the performance of All MTL as removing it often leads to performance improvement.


The following arguments and experiments are all with respect to the internal variability of K-fold CV.

as single train-test splits produce less stable prediction error estimates, our arguments are still valid for single data-splits but to an even greater degree.


Increasing K improves stability by averaging over more models.

each evaluation is performed on a smaller subset of the available data and so the evaluations themselves become less stable.


The combination of these competing variabilities makes up the total internal variance which, as confirmed by Figure 1, is nevertheless smaller than with single train-test splits.

if we have enough data that using 1/K th of the data still provides stable evaluations then we can see a reduction in internal variability as we increase K. this is not a general statement (Bengio and Grandvalet, 2004), as we will demonstrate in Section 4.


A further research interest is to investigate exactly when this assumption is violated and the effect that this can have on the effectiveness of parameter tuning.

increasing J has no effect on bias but does significantly reduce the internal variability.


When considering a larger incremental system, a processor usually seen as non-incremental might be incremental enough: If a language generation system works on the level of words, the grapheme to phoneme conversion does not need to be able to process sub-word input.

if input typed by a user should be vocalized, sub-word granularity might be needed.


Monotonicity limits the quality a component can produce as it can not revert a decision that turns out to be wrong later on in light of additional available input.

a non-monotonic component can always achieve the same non-incremental output as a non-incremental component by simply replacing all intermediate output with the one of the non-incremental component once all input is available.


As the manual transcription takes time, the system response is noticeably delayed in a non-incremental system where the dialogue system only starts to plan its response once transcription is complete.

the incremental system constructs a response as soon as possible, based on partial input.


Explicit corrections in spoken output are preferred by users to delay ( § 4.4), which could transfer to incremental MT.

lack of automatic evaluation likely requires an expensive human end-to-end evaluation due to the large deviation from non-incremental gold standard translations.


If the verb sense was neither in Propbank nor the list of previous decisions, annotators made note of the sense.

annotators were instructed to try to use the previous senses as much as possible.


Figure 1(a) shows a typical illustration of generation of t-th target word where the decoder takes three inputs, i.e., source context c t , previous target word y t−1 and current target state s t while generating one output y t via output state o t .

the study in  suggests that different target words require inconsistent contributions from source context (c t ) and target context (i.e., y t−1 and s t ).


Both the reset gate r t and the update gate z t control the amount of previous state h t−1 and current input Ex t being carried over to the current hidden state h t .

the gate itself either r t or z t , is uniformly set via fixed weights W r (or W z ) and U r (or U z ) for all inputs of Ex t and all previous states of h t−1 .


This holds in particular for the systems that employ discriminative (and sometimes global) features, which are not available in our system.


In the LSTM based model, the tokens are encoded/decoded using LSTM units, which can effectively summarize the temporal information of the sentences in source/target domains.

because of its recurrent nature, the LSTM based model is difficult to parallel, so the training efficiency becomes the major challenge.


(i) Most error types that appeared in English parsing also appeared in Vietnamese parsing.

the average errors per sentence in Vietnamese parsing were much larger than those in English, except for co-ordination.


The figure shows that the parsers' performance was improved when we eventually increased the training data from 1,337 to 6,337.

the F-score is almost saturated when the number of sentences was increased from 6,337 to 8,337 sentences.


The F-score of RNNGs-G especially achieved 78.2%, which was improved by 5.26% points in comparison with the use of an automatic POS tagger (PredictedPOS).

this figure does not indicate the error types that could be solved by improving POS tagging.


We can see that all of these four confusing POS pairs were potential contributors to bracketing errors that caused significant decreases in F-scores.

this table also indicates that the frequency of confusing POS pairs was not directly proportional to the contributions they made to parsing errors.


Our analysis results indicated that improving POS tagging was beneficial for some constructions, such as PP and clause attachments.

not all gold tags are helpful for solving the parsing errors, which means that the current 33 POS tags of the Vietnamese Treebank are not good enough for disambiguating constructions.


Neural models Mikolov et al., 2010;Józefowicz et al., 2016) have recently shown great improvement.

most of them share a common issue: a large output vocabulary implies a prohibitive computation time, due to the output normalization, along with a prediction challenge in a such high dimensional space.


This algorithm is theoretically proven to converge when the partition function is parametrized separately.

the scaling parameter is usually fixed and the un-normalized model therefore self-normalizes, as a side effect of the training procedure.


Since the goal is to approximate the data distribution with a model parametrized by θ, the conditional class distribution is defined as P (w|C = 1, H) = P θ (w|H) and P (w|C = 0, H) = P n (w), which gives the posterior class probabilities: and P (C = 0|w, H) = kP n (w) If σ denotes the sigmoid function, these equations can be rewritten as a logistic regression problem: The reformulation obtained in equation 7 shows that training a classifier based on a logistic regression will estimate the log-ratio of two distributions: this allows the learned distribution to be un-normalized, and the partition function to be parametrized separately 3 .

the partition function is context-dependent.


On the large corpus, the conventional NCE model doesn't even reach a perplexity under the size of the vocabulary, whereas augmenting k to 500 is not enough to reach a suitable perplexity.

increasing k reduces the computational benefit of NCE, even with k far inferior to the vocabulary size.


The rule based machine translation demands various kinds of linguistic resources such as morphological analyzer and synthesizer, syntactic parsers, semantic analyzers and so on.

corpus based approaches (as the name implies) require parallel and monolingual corpora.


As a result, notable progress towards the development and use of MT systems has been made for these languages.

research in the area of MT for Ethiopian languages, which are under-resourced as well as economically and technologically disadvantaged, has started very recently.


No single-data baseline exists for zero-shot translation since we have no parallel data for those pairs.


Models reviewed so far address the problem of morphology on the source side.

there is a group of models which study the same problem for the target side.


This is motivated by the nature of the task at hand: a document that is composed of high quality sentences is likely to have high quality as well.

simply aggregating sentence-level predictions is not a good strategy, as a document needs to be cohesive and coherent as a whole, i.e.


Table 3 also confirms the hierarchy between models on the FN 1.5 setup, albeit not as clearly as originally reported, with differences between NN and WSABIE models closer to 2 points of accuracy rather than 3.

table 4 shows that the hierarchy between the NN + SENtBOW and NN + DEPBOW models are not robust across datasets, as the NN + DEPBOW model performs better than the NN + SENtBOW model on the FN 1.7 setup, by a margin of .6 accuracy point overall.


In other words, if more than 32 target sentences are available, on average one should prefer monolingual parsing (at least when using BEAM) over KL-BEAM transfer: 15 the amount of knowledge transferred across languages seems very limited.

when considering the simple/complex scores, it appears that their difference is less pronounced for the cross-lingual parser than the monolingual ones, which results in distinct data-size equivalents.


Modern RNN architectures have been very successful in solving character-level NLP tasks (Chung et al., 2016b;Golub and He, 2016;Mikolov et al., 2012).

they do not make the learned linguistic structure explicit: rather it can be presumed to be cryptically encoded in the states of the hidden layers.


If this is not the case and the lower layer does not detect a boundary, the layer runs COPY.

if the lower layer does detect a boundary z −1 t , it runs UPDATE: if z t−1 = 0 and z −1 Output gate -The output embedding h e t of the HMLSTM is computed by the following gating mechanism: h e t = ReLU(g t W e h t ) It takes states from the three layers and computes a gating value for each g 1 t , g 2 t and g 3 t using the parameter vector w .


When the HMLSTM cell runs the FLUSH operation, it computes the new cell-state by i t u t and the previous cell-state is dropped from the computation.

both i t and u t depend on the previous state h t−1 .


The discrepancy could be due to the fact that the script that the authors shared with us was optimized for PTB, and some of the settings might need to be changed for Text8.

since we have not found such differences reported in the paper, we used the same setting for both datasets.


The proposed method is built on bag-of-characters (BOC) representation.

bOC is prone to anagrams and thus is susceptible to word collisions, i.e.


Furthermore, it has the lowest standard deviation across all runs, which means it is robust to parameter initialization.

both CNN and RNN models have lower performance and higher variability compared to the proposed method.


This could be one of the reasons why models using CNN seem to have superior performance when only the best performance is reported.

our model does not result in peaks or serious drops in performance with different seed values, which makes it more suitable for real-world applications.


Technical support problems are very complex.

to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like a detailed description of what is failing (symptom), steps taken in an effort to remediate the failure (activity), and sometimes a specific request or ask (intent).


Traditionally, this entails employing a large number of human agents to manually diagnose, troubleshoot, and resolve customer problems.

with ever increasing variety of products and the exponential growth of users, this model doesn't scale.


The key difference in these approaches and our problem setting is that they usually work on simpler, entity seeking questions.

questions in support are usually very complex, contain multiple attributes describing the situation that leads to a failure, attempts made to resolve the problem, and explicit asks.


We see a slight improvement in P@1 on the DB2 Prop dataset, and a 42.9% improvement in P@5.

on the DB2 Open dataset, we see very slight improvement on the P@1 and no improvement in P@5.


With the guidance of the information of global decoding, the model generates translation of higher accuracy and higher coherence.

as the deconvolution-based decoder is not responsible for generating translation, it is hard to interpret what each column of the generated matrix represents.


On one hand, in many different ways natural language is used to express the same information need.

the same word used in different sentences may express different meaning.


For a detailed description, we direct the reader to the original work.

we describe herein some elements which we have modified, a necessity given our changes to the taxonomy.


Entities that have been extracted through Wikification are often normalised "for free."

there is no simple way to get around this problem in the case of entities extracted through regular expressions, as in the case of numbers and dates where it is common for sentences to contain approximations.


Significant progress has been made in domains where a large amount of labeled training data is available.

obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data.


It has been widely studied and applied in many tasks (Yang and Mitchell, 2017;Liu et al., 2017).

its applicability to answer selection has yet to be Who played Dumbledore in Harry Potter?


Considering the example in Table 1, existing context-based models may assign a higher score to the negative answer than the positive answer, since the negative answer is more similar to the given question at word level.

with the background knowledge, we can correctly identify the positive answer based on the relative facts contained in the knowledge base (KB) such as (Dumbledore, played by, Michael Gambon), (Michael Gambon, cast in, Harry Potter).


Instead of learning the representations of the question and the answer separately, some recent studies exploit attention mechanisms to learn the interaction information between questions and answers, which can better focus on relevant parts of the input (Tan et al., 2016;.

these methods are subject to the amount of labeled data and the limited information provided by contexts.


Most recent studies in transfer learning for natural language processing employ deep neural networks to learn the shared feature representation between two different datasets (Mou et al., 2016;Li et al., 2017).

it was not until recent years that the application of transfer learning on QA received extensive attention.


We first employ n-gram matching to detect all the entity mentions in the sentence, and then retrieve a set of top-K entity candidates from KB for each entity mention.

the ambiguity issue of the entity is still remained to be tackled, e.g., Santiago can refer to a city or a person.


These annotators considered the question meaningless.

other annotators considered a scenario in which some teams could not finish the season.


Current state-of-the-art models also attempt to capture this by using the reading of the query to guide the reading of the document (Yang et al., 2017;Dhingra et al., 2017), or using the memory of the document to help interpret the query (Munkhdalai and Yu, 2017).

these systems only consider uni-directional dependencies.


At this scale, this dataset can support training and evaluating a machine learning-based fact checking system.

the usefulness of the dataset may be limited due the claims being provided without machine-readable evidence beyond originator metadata, meaning that systems can only resort to approaches to fact checking such as text classification or speaker profiling.


As with the claims collected by Vlachos and Riedel (2014), the size of the dataset prevents its use for training a machine learning-based fact checking system.

the broad range of types of claims in this dataset highlights a number of forms of misinformation to help identify the requirements for fact checking systems.


(2016) identify deceptive reviews also using lexical features.

rather than relying on labeled data, the authors induce labels over an unlabeled dataset through a semi-supervised learning approach that exploits a minimal amount labeled data from related tasks in a multi-task learning set up.


In the early stages of a rumour, its actual veracity tends to be unknown.

as new evidence emerges over time, Twitter users take more pronounced and continuously evolving stance towards the information asserted in the rumour.


For instance, in the early stages of a rumour supporting tweets might prevail, simply due to the lack of information to the contrary.

when authoritative sources or reliable evidence emerge either for or against the rumour, a similar trend tends to be observed in the collective rumour stances.


The dataset consists of rumours that emerged during eight different events.

three of these events evoked less than five rumours consisting of five or more tweets.


Further reducing sequences' length to five tweets leads to worse classification performance.

performance decrease is considerably lower for system λ with an F 1 score of 0.618 (λ: 0.524).


All tweets   Table 2 are obtained using gold stance labels.

this restricts the idea of stance-based rumour verification to only data where human stance labels are available.


Precision Recall F 1 λ a 0.632 0.888 0.738 λ a 0.669 0.975 0.794 Table 5: Overall scores using automatic labels In our results in Table 2 we showed that overall collective stance indeed is an important feature to consider for the purpose of veracity prediction.

this depends on how this collective feature is used.


In case of baseline B2 we used rather a crude way of capturing the stance wisdom by counting different stance types.

collective stance might obey some specific patterns of development, as indicated by Mendoza et al.


Since we seem to have overcome the need for manual annotation when using MSHMM in future work the data sets can be extended to more recent events featuring potentially large amounts of tweets.

it is also reasonable to assume that events are heterogeneous in their stance distribution patterns, which might have an impact on classification performance and generalizability across events.


This is a known problem that there is a performance drop when the system moves to new unseen data.

the performance gap can be closed with extending training data (obtained either manually or using some distance learning) and focusing more on domain independent features.


Stance detection has been extensively studied in recent years as a task to predict a stance of a user or text toward a specific topic (Murakami and Raymond, 2010;Mohammad et al., 2016;Persing and Ng, 2016).

most studies have depended heavily on labeled training data (Tutek et al., 2016;Liu et al., 2016).


Accordingly, the SemEval-2016 task 6B released unlabeled data on a topic to be predicted and labeled data on other topics (Mohammad et al., 2016).

the accuracy on the setting dropped drastically compared with the setting when labeled data for the topic are given.


They asked the following questions to the users of social media: whether they are interested in specific issues (e.g., healthcare cost and retirement); and whether they have posted on those issues on Twitter or Facebook.

to their work covering only seven issues, we deal with more than 1000 topics.


Note that, it would be preferable to evaluate users who did not declare stances at all as the silent majority.

since it is virtually impossible for a third person to annotate stances of such users, we defined the pseudo silent majority for this experiment.


Therefore, our method is useful for analyzing opinions, including those of the silent majority.

the performance of the stance detection in this work is not yet sufficient.


Until now, computational approaches for fake news detection have relied on satirical news sources such as "The Onion" (Rubin et al., 2016), viral news tracking websites such as BuzzFeed (Potthast et al., 2017) and fact-checking websites such as "politiFact" (Wang, 2017) and "Snopes" (Popat et al., 2016).

the use of these sources poses several challenges and potential drawbacks.


The best classification performances were achieved with feature sets representing absurdity, punctuation, and grammar.

fact-checking approaches rely on automated verification of propositions made in the news articles (e.g., "Barack Obama assumed office on a Tuesday") to assess the truthfulness of their claims .


Although fact-checking approaches are becoming increasingly powerful, a major drawback is that they are built on the premise that the information can be verified using external sources, for instance FakeCheck.org and Snopes.com.

this is not a straightforward task, as external sources might not be available, particularly for just-published news items.


Specifically, legitimate news in tabloid and entertainment magazines seem to use more first person pronouns, talk about time (Relativity,Time, FocusPast), and use positive emotion words (posemo), which interestingly were also found as markers of truth-tellers in previous work on deception detection (Pérez-Rosas and Mihalcea, 2014).

fake content in this domain has a predominant use of second person pronouns (he, she), negative emotion words (negemo) and focus on the present (Foc.Pres).


(2015) and Enayet and El-Beltagy (2017) have created successful models using this premise.

these studies assume access to stance and veracity labels for the same data, which does not apply in our case as we do not have stance labels for all of the threads in the dataset (see section 3).


As the datasets contain a significant class imbalance, the majority baseline achieves fairly high accuracy scores.

due to the nature of this task, it is more important for a model to recognize all of the classes, especially false rumours, therefore the macro-averaged F-score is more important for performance evaluation.


Standard word embedding algorithms learn vector representations from large corpora of text documents in an unsupervised fashion.

the quality of word embeddings learned from these algorithms is affected by the size of training data sets.


While it may seem that this model is similar to SWESA, it is not so because in their model (Maas et al., 2011) first learn word embeddings and then fit a classifier in two different objective functions.


SWESA can be roughly mapped to the SDL by considering dictionary D of size k × V , where each column corresponds to a word embedding.

there are significant differences between SWESA and SDL.


This result is not surprising, given that the pre-trained RNTN and CNNS are i)initialized with pre-trained word embeddings and ii)trained on a data set with roughly 10 times more training data points than within SWESA.

note that on the imbalanced A-CHESS data set RNTN fails to perform any classification.


These representations are then used in supervised models for various classification tasks.

such tasks sometimes require very specific features that may not have been captured by the unsupervised objective.


The baseline method retrieved rather difficult texts, in which only 72.91% of the words were non-complex, below the desired minimum rate of 80%.

the WordList model and Learner+WordList model retrieved texts with 80.60% and 86.26% of non-complex words, respectively.


In the previously mentioned experiments we focused on more abstract features (POS tags), which we combine with punctuation marks to capture a higher-level representation of punctuation usage.

boW/word n-gram features are the ones that lead to the best results on the task, even though they have been disputed as being useful (brooke and Hirst, 2012), but potentially overfitting (brooke and Hirst, 2011).


(2012), L2 English learners are confident about their use of punctuation.

the high improvement for high-proficiency learners in both imbalanced and balanced settings suggests that learners keep their L1 punctuation style even when achieving high English proficiency.


By the end of this process, we identified structures that could be considered problematic in terms of writing for a language learner in each of the three levels.

the divergence score gave us only the magnitude of the divergence, but it did not account for where the divergence exactly lies, and, for our purposes, we deemed important to know the direction of the divergence, i.e., to know whether a structure is more prominent in the reception or the production.


for the performance of entities containing multiple words, the Fisher vector is better than word embedding average in the perspective of F-score.


Each news article annotated the discourse frame, structure and relationship.

the discourse units are also smaller with a minimum of one sentence and a maximum of five sentences, and their research object is primarily at the word level.


Due to the increase in the number of channels and the need for more customized content for different audience segments, content authors need to produce content at a very rapid pace.

a lot of such content has already been produced and is available in enterprise content repositories.


Given an enterprise author's requirement in the form of a snippet describing the content she is trying to build, standard querying mechanisms can be utilized to fetch the right set of articles that can cater to her needs.

constructing an initial piece of 'article' that can cater to her needs would still be a non-trivial task.


The content style in an enterprise corpus is fairly consistent.

there could be multiple representations of the same information across the repository.


(2017) extend standard extractive summarization techniques to build article by combining different paragraphs in a corpus.

they do not account for coherence or information redundancies in the generated content.


(2011) aim to enhance question-answering by expanding a 'seed document' using web resources to construct the answer based on 'paragraph nuggets'.

both lexical and semantic redundancies are undesirable for a content author.